Next Generation of Explainable Intelligence
We build technologies that reconstruct structure — and keep it trustworthy under adversarial input
We map raw signals, code, and documents into semantic structures — with explainable decisions, resilient behavior under drift, and deterministic safety boundaries for agentic systems.
OUR TECHNOLOGIES
Products built on structural intelligence
Three entry points: architecture drift in code review, CI failure autopsy, and runtime trust boundaries for agentic pipelines.
Detects structural anomalies in pull requests. Keeps your repo’s architecture aligned — automatically. Not another linter. A signal layer for architectural review.
GitHub PR workflow
Repo-specific baseline
High signal
CI Autopsy posts a GitHub Check with root cause, key lines, a focused log excerpt, and Do now. If evidence is insufficient, it chooses abstain and tells you what’s missing for a confident call.
GitHub PR workflow
Repo-specific baseline
High signal
OUR OPEN SOURCE
Core building blocks of the paradigm
Foundational components we ship publicly — to make the stack reproducible and composable.
Deterministic trust-boundary layer that treats untrusted text as “pressure”, accumulates session risk, and triggers auditable Off decisions.
Blocks prompt injection, secret exfiltration, and tool abuse — with attribution to the exact chunks.
Open source
RAG / Agents
Prompt injection defense
PIF maps any data — tables, images, signals, actions — into a shared phase space. This allows systems to connect modalities, find recurring patterns, and return results back into discrete form.
Open source
Interchange format
Framework
OUR RESEARCH FOUNDATUIN
Publications that define the stack
A research-driven approach to explainability: structure first, semantics as a consequence.
Publication · Feb 22, 2026
© 2026 • Build systems that reconstruct the structure of reality